Publications

Zhong, LH; Hu, L; Zhou, H; Tao, X (2019). Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US. REMOTE SENSING OF ENVIRONMENT, 233, UNSP 111411.

Abstract
Winter wheat is a major staple crop and it is critical to monitor winter wheat production using efficient and automated means. This study proposed a novel approach to produce winter wheat maps using statistics as the training targets of supervised classification. Deep neural network architectures were built to link remotely sensed image series to the harvested areas of individual administrative units. After training, the resultant maps were generated using the activations on a middle layer of the deep model. The direct use of statistical data to some extent alleviates the shortage of ground samples in classification tasks and provides an opportunity to utilize a wealth of statistical records to improve land use mapping. The experiments were carried out in Kansas and Northern Texas during 2001-2017. For each study area the goal was to create winter maps that are consistent with USDA county-level statistics of harvested areas. The trained deep models automatically identified the seasonal pattern of winter wheat pixels without using pixel-level reference data. The winter wheat maps were compared with the Cropland Data Layer (CDL) for years when the CDL is available. In Kansas where the winter wheat extent of the CDL has high reported accuracy and agrees well with county statistics, the maps produced from the deep model was evaluated using the CDL as an independent test set. Northern Texas was selected as an example where the winter wheat area of the CDL is very different from official statistics, and the maps by the deep model enabled a map-to-map comparison with the CDL to highlight the areas of discrepancy. Visual representation of the deep model behaviors and recognized patterns show that deep learning is an automated and robust means to handle the variability of winter wheat seasonality without the need of manual feature engineering and intensive ground data collection. Showing the possibility of generating maps solely from regional statistics, the proposed deep learning approach has great potential to fill the historical gaps of conventional sample-based classification and extend applications to areas where only regional statistics are available. The flexible deep network architecture can be fused with various statistical datasets to fully employ existing sources of data and knowledge.

DOI:
10.1016/j.rse.2019.111411

ISSN:
0034-4257